Abstract:
In this paper we consider the problem of creating a
two dimensional spatial representation of gas distribution with a
mobile robot. In contrast to previous approaches to the problem
of gas distribution mapping (GDM) we do not assume that
the robot has perfect knowledge about its position. Instead we
develop a probabilistic framework for simultaneous localisation
and occupancy and gas distribution mapping (GDM/SLAM)
that allows to account for the uncertainty about the robot’s
position when computing the gas distribution map. Considering
the peculiarities of gas sensing in real-world environments, we
show which dependencies in the posterior over occupancy and
gas distribution maps can be neglected under certain practical
assumptions. We develop a Rao-Blackwellised particle filter
formulation of the GDM/SLAM problem that allows to plug in
any algorithm to compute a gas distribution map from a sequence
of gas sensor measurements and a known trajectory. In this paper
we use the Kernel Based Gas Distribution Mapping (Kernel-
GDM) method. As a first step towards outdoor gas distribution
mapping we present results obtained in a large, uncontrolled,
partly open indoor environment.